super(channel_selection, self).__init__() self.indexes = nn.Parameter(torch.ones(num_channels))
时间: 2024-05-23 08:15:53 浏览: 121
这两行代码是一个类的构造函数,用于初始化类的属性。第一行代码调用了父类的构造函数并传递了 `channel_selection` 参数,以便父类可以正确地初始化。第二行代码创建了一个名为 `indexes` 的可训练参数(`nn.Parameter`),它是一个大小为 `num_channels` 的张量,每个元素都被初始化为 1。这个参数在模型训练时会被动态更新,用于选择输入数据中的哪些通道参与计算。
相关问题
def __getitem__(self, index): if self.args.cached: (label, _, image) = self.data[index] else: (label, _file) = self.file_indexes[index] image = self._load_rml(_file) n_label = self.classes.index(label) return torch.tensor(image).float(), torch.tensor(n_label).long()把这段代码基于pytorch改为基于mindspore
以下是基于MindSpore的代码实现:
```
import mindspore.dataset as ds
import mindspore.dataset.transforms.py_transforms as py_transforms
import mindspore.common.dtype as mstype
import numpy as np
class MyDataset:
def __init__(self, args):
self.args = args
self.classes = sorted(list(set([item[0] for item in self.args.data])))
self.data = self.args.data
self.file_indexes = [(item[0], idx) for idx, item in enumerate(self.args.data)]
def _load_rml(self, file):
# 加载数据的代码
pass
def __getitem__(self, index):
if self.args.cached:
(label, _, image) = self.data[index]
else:
(label, file_idx) = self.file_indexes[index]
image = self._load_rml(self.args.data[file_idx][1])
n_label = self.classes.index(label)
return np.array(image).astype(np.float32), np.array(n_label).astype(np.int32)
def __len__(self):
return len(self.file_indexes)
# 数据增强
transform = py_transforms.Compose([
py_transforms.Resize((224, 224)),
py_transforms.RandomHorizontalFlip(),
py_transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# 数据集加载
ds.config.set_seed(1)
ds_train = ds.GeneratorDataset(MyDataset(args), ["image", "label"])
ds_train = ds_train.shuffle(buffer_size=1000)
ds_train = ds_train.batch(batch_size=args.batch_size, drop_remainder=True)
ds_train = ds_train.map(operations=transform, input_columns="image", num_parallel_workers=4)
ds_train = ds_train.map(operations=lambda x, y: (mindspore.Tensor(x, mstype.float32), mindspore.Tensor(y, mstype.int32)))
```
注意:MindSpore的数据增强需要使用`transforms`模块中的函数,而数据集加载则需要使用`GeneratorDataset`类。在MindSpore中,需要使用`mindspore.Tensor`将数据转换为张量类型。
def forward(self, data, org_edge_index): x = data.clone().detach() edge_index_sets = self.edge_index_sets device = data.device batch_num, node_num, all_feature = x.shape x = x.view(-1, all_feature).contiguous() gcn_outs = [] for i, edge_index in enumerate(edge_index_sets): edge_num = edge_index.shape[1] cache_edge_index = self.cache_edge_index_sets[i] if cache_edge_index is None or cache_edge_index.shape[1] != edge_num*batch_num: self.cache_edge_index_sets[i] = get_batch_edge_index(edge_index, batch_num, node_num).to(device) batch_edge_index = self.cache_edge_index_sets[i] all_embeddings = self.embedding(torch.arange(node_num).to(device)) weights_arr = all_embeddings.detach().clone() all_embeddings = all_embeddings.repeat(batch_num, 1) weights = weights_arr.view(node_num, -1) cos_ji_mat = torch.matmul(weights, weights.T) normed_mat = torch.matmul(weights.norm(dim=-1).view(-1,1), weights.norm(dim=-1).view(1,-1)) cos_ji_mat = cos_ji_mat / normed_mat dim = weights.shape[-1] topk_num = self.topk topk_indices_ji = torch.topk(cos_ji_mat, topk_num, dim=-1)[1] self.learned_graph = topk_indices_ji gated_i = torch.arange(0, node_num).T.unsqueeze(1).repeat(1, topk_num).flatten().to(device).unsqueeze(0) gated_j = topk_indices_ji.flatten().unsqueeze(0) gated_edge_index = torch.cat((gated_j, gated_i), dim=0) batch_gated_edge_index = get_batch_edge_index(gated_edge_index, batch_num, node_num).to(device) gcn_out = self.gnn_layers[i](x, batch_gated_edge_index, node_num=node_num*batch_num, embedding=all_embeddings) gcn_outs.append(gcn_out) x = torch.cat(gcn_outs, dim=1) x = x.view(batch_num, node_num, -1) indexes = torch.arange(0,node_num).to(device) out = torch.mul(x, self.embedding(indexes)) out = out.permute(0,2,1) out = F.relu(self.bn_outlayer_in(out)) out = out.permute(0,2,1) out = self.dp(out) out = self.out_layer(out) out = out.view(-1, node_num) return out
这是一个PyTorch模型的前向传播函数,它接受两个参数:data和org_edge_index。该函数使用GCN(图卷积神经网络)来处理输入数据,并返回一个输出张量。具体实现细节可以参考代码中的注释。
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